As a subclass of nanosatellites, CubeSats have changed the game’s rules in the scientific research industry and the development of new space technologies. The main success factors are their cost effectiveness, relative ease of production, and predictable life cycle. CubeSats are very important for training future engineers: bachelor’s and master’s students of universities. At the same time, using CubeSats is a cost-effective method of nearest space exploration and scientific work. However, many issues are related to efficient time-limited development, software and system-level quality assurance, maintenance, and software reuse. In order to increase the flexibility and reduce the complexity of CubeSat projects, this article proposes a “hybrid” development model that combines the strengths of two approaches: the agile-a-like model for software and the waterfall model for hardware. The paper proposes a new computing platform solution, “Falco SBC/CDHM”, based on Microchip (Atmel) ATSAMV71Q21 with improved performance. This type of platform emphasizes low-cost space hardware that can compete with space-grade platforms. The paper substantiates the architecture of onboard software based on microservices and containerization to break down complex software into relatively simple components that undergraduates and graduates can handle within their Master’s studies, and postgraduates can use for scientific space projects. The checking of the effectiveness of the microservice architecture and the new proposed platform was carried out experimentally, involving the time spent on executing three typical algorithms of different algorithmic complexities. Algorithms were implemented using native C (Bare-metal) and WASM3 on FreeRTOS containers on two platforms, and performance was measured on both “Falco” and “Pi Pico” by Raspberry. The experiment confirmed the feasibility of the complex application of the “hybrid” development model and microservices and container-based architecture. The proposed approach makes it possible to develop complex software in teams of inexperienced students, minimize risks, provide reusability, and thus increase the attractiveness of CubeSat student projects.

The subject of study in this article is a method for industrial equipment vibration diagnostics that uses discrete Fourier transform and Allan variance to increase precision and accuracy of industrial equipment vibration diagnostics processes. We propose IoT-oriented solutions based on smart sensors. The primary objectives include validating the practicality of employing platform-oriented technologies for vibro-diagnostics of industrial equipment, creating software and hardware solutions for the IoT platform, and assessing measurement accuracy and precision through the analysis of measurement results in both time and frequency domains. The IoT system architecture for industrial equipment vibration diagnostics consists of three levels. At the autonomous sensor level, vibration acceleration indicators are obtained and transmitted via a BLE digital wireless data transmission channel to the second level, the hub, which is based on a BeagleBone single-board microcomputer. The computing power of BeagleBone is sufficient to work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the state of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is used. The verification of the accuracy and precision of the vibration diagnostics system was carried out on the developed stand. A comparison of the expected and measured results in the frequency and time domains confirms the correct operation of the entire system.

The paper presents an academic case where the authors describe elaborated design of the course “Software Engineering Sustainability” to introduce the sustainable concept into educational programs for software engineers. Presented course design considers requirements of employers, maintains the successful experience of national higher education and professional industry standards, and conforms to the European Union e-Competence Framework. The authors present the design of the course with a description of the course content and visible positioning Green Information and Communication Technology (ICT). The course has been being delivered by the first author at National Aerospace University “Kharkiv Aviation Institute”.

A primary purpose of this work is to create an application to help to navigate for visually impaired people using urban transport. The relevance of the topic was established by analyzing the transport accessibility for visually challenged people. As a result, a list of user requirements was determined. Analysis of the market and available analogs revealed the main advantages and disadvantages of existing software systems. The basis of the architectural decision is the principle of responsibility distribution. The software was created using the following technologies: Google Cloud Service, Google Speech-To-Text, Google Geocode API, Google Directions API, Google Maps API, Xamarin, Firebase, ASP.NET MVC.

The amount of data increases exponentially every year, the nomenclature of data is expanded; unstructured data reaches so largely that traditional methods and approaches of data processing cannot be used. Challenges are that it is necessary to ensure the preservation and management of a huge amount of data in order to be able to efficiently analysis for solving business tasks, but also to analyze and extract valuable value from it. Today it is important to upgrade existing Data Warehouses, which will help to effectively use unstructured data. The proposed data storage reengineering methods are based on two expert rules approaches: indexing (to avoid viewing very large volumes of data) and sorting (storing data in the required order to speed up the most common queries). These methods are a partial and intermediate solution on the way to modern data processing technologies because the current technology for Big Data allows organizations to dramatically improve return on investment from their existing data warehouse environment.